เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| การออกแบบการทดลองกลุ่มควบคุมแบบครอสโอเวอร์× | การออกแบบการทดลองแบบมีกลุ่มควบคุม× | |
|---|---|---|
| สาขาวิชา | การออกแบบการทดลอง | การออกแบบการทดลอง |
| ตระกูล | Process / pipeline | Process / pipeline |
| ปีกำเนิด≠ | Mid-20th century; systematic treatment from 1980s onward | 1935 (Fisher); 1963 (Campbell & Stanley codification) |
| ผู้ริเริ่ม≠ | Established in clinical pharmacology and agricultural research; formalized by B. Jones & M. G. Kenward | Ronald A. Fisher; systematised by Donald T. Campbell & Julian C. Stanley |
| ประเภท≠ | Experimental design | Experimental research design |
| แหล่งต้นตำรับ≠ | Jones, B., & Kenward, M. G. (2003). Design and Analysis of Cross-Over Trials (2nd ed.). Chapman and Hall/CRC. ISBN: 978-1584883500 | Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally. link ↗ |
| ชื่อเรียกอื่น | crossover controlled trial, within-subject crossover with control, AB/BA crossover controlled design, repeated-measures crossover with control arm | controlled experiment, true experimental design, randomized controlled design, treatment-control design |
| ที่เกี่ยวข้อง≠ | 6 | 4 |
| สรุป≠ | A crossover control group experimental design is an experimental approach in which participants are randomly assigned to sequences of conditions that include both a treatment and a control (no-treatment or placebo) period, with each participant experiencing both the experimental and control conditions in succession. By using each participant as their own control across periods, this design sharply reduces between-subject variability and typically requires fewer participants than parallel group trials to achieve equivalent statistical power. | Control group experimental design is a fundamental experimental structure in which participants are assigned to at least two groups — a treatment group that receives the intervention and a control group that does not — so that the effect of the intervention can be isolated by comparing outcomes across groups. Randomisation of assignment strengthens causal inference by balancing known and unknown confounders. |
| ScholarGateชุดข้อมูล ↗ |
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